Nymphstar: An accurate high‐throughput quantitative method for whitefly (Aleurotrachelus socialis Bondar) resistance phenotyping in cassava

Abstract

AbstractWhitefly (Aleurotrachelus socialis Bondar) is a major pest causing significant economic losses in cassava production systems in North South America. It diminishes cassava's photosynthesis by colonizing leaves, directly feeding on phloem sap, or excreting substances that foster sooty mold growth, reducing the photosynthetic area. The most effective pest management approach is deploying natural resistance in the crop. Identifying germplasm with superior whitefly‐resistance (WFR) through phenotypic evaluation distinguishing it from whitefly‐susceptible responses requires an accurate, high‐throughput, quantitative phenotyping method. We developed Nymphstar, an image‐based phenotyping tool, as an ImageJ plugin, quantifying third‐ and fourth‐instar nymphs and their leaf area they occupy through red, green, and blue color space analysis. Using Nymphstar, we tested 19 cassava genotypes and classified their resistance to A. socialis. The plugin proved efficient, completing the analysis in 25.56 min on average for the entire dataset. In contrast, manual counting for the same set of images took 425.23 min on average averaging around 6.29 min/image. Nymphstar was ∼17 times faster showcasing its efficiency. To assess WFR in cassava germplasm, we conducted a full‐bench caging free‐choice assay. This approach enhanced whitefly colonization on each cassava genotype, providing an accurate representation of resistance/susceptible while reducing operator bias. Nymphstar is a rapid, precise tool for automated nymphs counting and leaf area quantification. It facilitates the large‐scale assessment of cassava resistance to whitefly, eliminating bias associated with field assessment and manual counting.